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Current

Lead Data Scientist

Sorry, this job was removed at 06:21 p.m. (EST) on Sunday, Feb 09, 2025
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New York, NY, USA
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New York, NY, USA

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Lead Data Scientist

At Current, we’re on a mission to enable our members to create better financial outcomes for themselves. Headquartered in NYC, we’re a leading U.S. fintech and one of the fastest growing companies with over 4 million members. No matter your title, we’re a team that collaborates on building great products and making an impact together.

We are in search of an experienced Lead Data Scientist.  In this role, you will lead the design, development, and deployment of machine learning models and algorithms, leveraging VertexAI to solve complex business challenges in the fintech domain. The ideal candidate will possess a strong background in statistical analysis, machine learning techniques, programming (), and preferably a deep understanding of VertexAI’s capabilities along with experience in deploying and scaling models using Google Dataflow.

Responsibilities:

  • Analyze diverse datasets to extract meaningful insights and patterns, identifying actionable opportunities for optimization and innovation.
  • Design, develop, and deploy predictive, clustering, recommendation system and anomaly detection models and algorithms ( Preferably using VertexAI) for various applications, including customer segmentation, fraud detection, credit risk assessment, and personalized recommendations.
  • Conduct exploratory data analysis, feature engineering, and model selection to optimize performance and enhance predictive accuracy.
  • Implement and maintain scalable machine learning pipelines and workflows on Google Cloud Platform, ensuring reliability, scalability, and efficiency.
  • Leverage Google Dataflow for processing large-scale data and building scalable data processing pipelines.
  • Evaluate model performance using appropriate metrics and techniques, conducting A/B testing and iterating on models to continuously improve accuracy and business impact.
  • Collaborate with engineering teams to integrate machine learning models into production systems, monitor performance, and troubleshoot issues.
  • Contribute to the development of best practices, standards, and documentation for machine learning processes and methodologies

Requirements:

  • 7+ years of experience in data science or machine learning roles, with expertise in developing and deploying machine learning models. 
  • Degree in Computer Science, Statistics, Mathematics, or related field.
  • Proficiency in Python programming and machine learning libraries (e.g., TensorFlow, scikit-learn) for model development and deployment.
  • Experience with data preprocessing, feature engineering, and model selection techniques to optimize performance.
  • Familiarity with Google Cloud Platform services, particularly VertexAI and Dataflow, for scalable data processing and model training. 
  • Proficiency in Scala programming language for developing scalable and efficient machine learning solutions is preferred.
  • Strong understanding of statistical analysis, hypothesis testing, and experiment design.
  •  Excellent problem-solving skills with the ability to translate business requirements into technical solutions.
  • Strong communication and collaboration skills with the ability to work effectively in a fast-paced, cross-functional environment.

Preferred:

  • Experience using Scala programming language for developing scalable and efficient machine learning solutions.
  • 2+ years of experience building and deploying models using Vertex AI.
  • Experience in B2C Fintech or Financial Services domain(s).

This role has a base salary range of $190,000- $228,000. Compensation is determined based on experience, skill level, and qualifications, which are assessed during the interview process. Current offers a competitive total rewards package which includes base salary, equity, and comprehensive benefits.

Benefits:

  • Competitive salary 
  • Stock options 
  • 401(k) savings plan 
  • Discretionary performance bonus program 
  • Biannual performance reviews
  • Medical, Dental and Vision premiums covered at 100% for you and your dependents 
  • Unlimited time off and paid holidays 
  • Generous parental leave policy
  • Commuter benefits 
  • Healthcare and Dependent care FSA benefit 
  • Employee Assistance Programs focused on mental health 
  • Healthcare advocacy program for all employees 
  • Access to mental health apps 
  • Team building activities
  • Our modern Chelsea-based office with open floor plan, stocked kitchen, and catered lunches


HQ

Current New York, New York, USA Office

Our modern, open floor plan office is located in the heart of Chelsea with an abundance of bars and restaurants. We are minutes away from various trains and bus stops for a convenient commute.

What you need to know about the NYC Tech Scene

As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.

Key Facts About NYC Tech

  • Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
  • Key Industries: Artificial intelligence, Fintech
  • Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
  • Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory

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